{
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8bW5uwdFfj5K"
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SONAL BANSAL\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "  from numpy.core.umath_tests import inner1d\n"
     ]
    }
   ],
   "source": [
    "import random,math,copy,time\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import (RandomForestClassifier,GradientBoostingClassifier)\n",
    "from sklearn import tree\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split,cross_val_score\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import preprocessing, svm\n",
    "from tqdm import tqdm\n",
    "import time\n",
    "from sklearn.model_selection import GridSearchCV\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Import up sound alert dependencies\n",
    "from IPython.display import Audio, display\n",
    "\n",
    "def allDone():\n",
    "    display(Audio(url='https://sound.peal.io/ps/audios/000/000/707/original/Good_Job!.wav', autoplay=True))\n",
    "## Insert whatever audio file you want above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "S6-dFa8Jfj5Q"
   },
   "outputs": [],
   "source": [
    "m=20  # Problem dimension (number of decision variables\n",
    "Ap=0.1  # Awareness probability\n",
    "fl=2   # Flight length (fl)\n",
    "wf=1.2# wf is used to control the importance of classification accuracy and number of selected features.\n",
    "d=67600# Flock (population) size\n",
    "lt=67600"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VSQ4rCRjfj5V"
   },
   "outputs": [
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     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "df=pd.read_csv(\"MCI-CN.csv\")\n",
    "df = df.dropna(how='all')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yBZV56q2m6-s"
   },
   "outputs": [],
   "source": [
    "df=df.drop(columns=['Unnamed: 0'])\n"
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   "source": [
    "df.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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     "execution_count": 4,
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   ],
   "source": [
    "val_Data = pd.read_csv(\"MCI-CN_val.csv\")\n",
    "val_Data = val_Data.dropna(how='all')\n",
    "#val_Data=val_Data.drop(columns=['Unnamed: 0'])\n",
    "\n",
    "val_Data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
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      "dtypes: int64(67601)\n",
      "memory usage: 273.9 MB\n"
     ]
    }
   ],
   "source": [
    "val_Data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_W4D-XnDfj5j"
   },
   "outputs": [],
   "source": [
    "features = [i for i in range(0,67600)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VvAFiZhvfj5n"
   },
   "outputs": [],
   "source": [
    "\n",
    "y_train=df['label']\n",
    "x_train=df[df.columns[1:]]\n",
    "x_train.columns=features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "K-e_XSa4pKr8"
   },
   "outputs": [],
   "source": [
    "y_test = val_Data['label']\n",
    "x_test = val_Data[val_Data.columns[1:]]\n",
    "x_test.columns=features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## WITHOUT GRID-SEARCH AND WITHOUT BIO-INSPIRED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Kgvbr_X7fj51"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1553592908.84151\n",
      "0.6308851224105462\n",
      "0.664783427495292\n",
      "0.7080979284369114\n",
      "0.6836158192090396\n"
     ]
    }
   ],
   "source": [
    "print(time.time())\n",
    "\n",
    "neigh1 = KNeighborsClassifier()\n",
    "neigh1.fit(x_train, y_train)\n",
    "neigh1.predict(x_test)\n",
    "nacc1=neigh1.score(x_test,y_test)\n",
    "\n",
    "print(nacc1)\n",
    "\n",
    "#\n",
    "rforest1 = RandomForestClassifier()\n",
    "rforest1.fit(x_train, y_train)\n",
    "rforest1.predict(x_test)\n",
    "rfacc1=rforest1.score(x_test,y_test)\n",
    "\n",
    "print(rfacc1)\n",
    "\n",
    "#\n",
    "dtree1 = tree.DecisionTreeClassifier()\n",
    "dtree1.fit(x_train, y_train)\n",
    "dtree1.predict(x_test)\n",
    "dtacc1=dtree1.score(x_test,y_test)\n",
    "\n",
    "print(dtacc1)\n",
    "\n",
    "clf1 = svm.SVC()\n",
    "clf1.fit(x_train, y_train)\n",
    "clf1.predict(x_test)\n",
    "clfacc1=clf1.score(x_test,y_test)\n",
    "\n",
    "print(clfacc1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. ... 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "im=(rforest1.feature_importances_)\n",
    "print(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                                                        | 0/67600 [00:00<?, ?it/s]C:\\Users\\SONAL BANSAL\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "100%|███████████████████████████████████████████████████████████████████████████| 67600/67600 [08:17<00:00, 135.75it/s]\n"
     ]
    }
   ],
   "source": [
    "imo=pd.Series()\n",
    "for i in tqdm(range(d)):\n",
    "    imo= imo.set_value(features[i],im[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67600"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imo.to_csv('importance_feature_MCI-CN.csv')\n",
    "len(imo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## WITH GRID-SEARCH WITHOUT BIO-INSPIRED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 108 candidates, totalling 324 fits\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=best, total= 1.0min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  1.1min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=best, total= 1.4min\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=best, total= 1.4min\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=random, total=  14.4s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=random, total=  19.2s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=random, total=  19.9s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=10, splitter=best, total=  59.3s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=10, splitter=best \n",
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      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=20, splitter=best \n",
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      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=5, splitter=random, total=  14.9s\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=5, splitter=random, total=  18.7s\n",
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      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=5, splitter=random, total=  21.3s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=5, splitter=random \n",
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      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=10, splitter=best, total= 1.5min\n",
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      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=5, splitter=best, total= 1.3min\n",
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      "[CV] criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=5, splitter=random, total=  20.7s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=10, splitter=best, total= 1.8min\n",
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      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=10, splitter=best, total= 1.4min\n",
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      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=10, splitter=random, total=  22.1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=10, splitter=random, total=  18.0s\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=20, splitter=best, total= 1.3min\n",
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      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=best, total=  59.8s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=best, total= 1.3min\n",
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      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=random, total=  14.3s\n",
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      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=10, splitter=best, total= 1.1min\n",
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      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=5, splitter=best, total= 1.0min\n",
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      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=5, splitter=best, total= 1.2min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=5, splitter=random, total=  14.9s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=5, splitter=random \n",
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      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=5, splitter=best \n",
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      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=5, splitter=best, total= 1.3min\n",
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      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=5, splitter=best, total= 1.6min\n",
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      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=10, splitter=best, total= 1.4min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=10, splitter=best, total= 1.6min\n",
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      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=10, splitter=random, total=  16.5s\n",
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      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=10, splitter=random, total=  13.9s\n",
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      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=20, splitter=best, total= 1.4min\n",
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      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=20, splitter=random, total=  17.2s\n",
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      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=5, splitter=best, total= 1.4min\n",
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      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=5, splitter=best, total= 1.3min\n",
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      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=5, splitter=best, total= 1.5min\n",
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      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=5, splitter=random, total=  15.8s\n",
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      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=10, splitter=best, total= 1.4min\n",
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      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=5, splitter=best, total= 1.3min\n",
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      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=10, splitter=best, total= 1.7min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=10, splitter=random, total=  23.1s\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=20, splitter=best \n",
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      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=4, min_samples_split=5, splitter=best, total= 1.7min\n",
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      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=10, splitter=random, total=  22.6s\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=best, total= 1.6min\n",
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      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=best, total= 1.2min\n",
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      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=best, total=  56.7s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=random, total=  13.2s\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=random, total=  15.6s\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=random, total=  17.8s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 324 out of 324 | elapsed: 280.7min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6509872241579558\n",
      "{'criterion': 'entropy', 'max_depth': 20, 'min_samples_leaf': 2, 'min_samples_split': 5, 'splitter': 'random'}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio controls=\"controls\" autoplay=\"autoplay\">\n",
       "                    <source src=\"https://sound.peal.io/ps/audios/000/000/707/original/Good_Job!.wav\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dtree1 = tree.DecisionTreeClassifier()\n",
    "dtparams = {'criterion':['gini', 'entropy'], 'splitter':['best','random'], 'max_depth':[10,20,30], 'min_samples_split':[5,10,20], 'min_samples_leaf':[2,4,6]}\n",
    "grid_dt = GridSearchCV(dtree1, dtparams, verbose=2)\n",
    "grid_dt.fit(x_train, y_train)\n",
    "grid_dt.predict(x_test)\n",
    "print(grid_dt.best_score_)\n",
    "print(grid_dt.best_params_)\n",
    "allDone()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 324 candidates, totalling 972 fits\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=10 \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=10, total=   4.6s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    5.6s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=10 \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=10, total=   4.1s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=10 \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=10, total=   3.9s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100 \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100, total=  16.1s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100 \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100, total=  17.1s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100 \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100, total=  18.9s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200 \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200, total=  28.0s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200 \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200, total=  33.9s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200 \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200, total=  40.6s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300 \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300, total=  45.8s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300 \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300, total=  52.1s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300 \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300, total=  46.9s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=400 \n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=20, n_estimators=100 \n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300 \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=  40.8s\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=5, n_estimators=400 \n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=20, n_estimators=500 \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=20, n_estimators=500, total= 1.2min\n",
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      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=500, total= 1.5min\n",
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      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=400, total= 1.2min\n",
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      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=400, total= 1.2min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=20, n_estimators=100 \n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=  32.2s\n",
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      "[CV] criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=20, n_estimators=300 \n",
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      "[CV] criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=20, n_estimators=300 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=20, n_estimators=300, total=  47.6s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=20, n_estimators=400 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=20, n_estimators=400, total= 1.1min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=20, n_estimators=400 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=20, n_estimators=400, total= 1.1min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=20, n_estimators=400 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=20, n_estimators=400, total= 1.1min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=20, n_estimators=500 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=20, n_estimators=500, total= 1.4min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=20, n_estimators=500 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=20, n_estimators=500, total= 1.4min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=20, n_estimators=500 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=20, n_estimators=500, total= 1.4min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=10 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=10, total=   4.1s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=10 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=10, total=   3.9s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=10 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=10, total=   4.0s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=100 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=100, total=  18.8s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=100 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=100, total=  17.9s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=100 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=100, total=  18.9s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=200 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=200, total=  35.6s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=200 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=200, total=  30.2s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=200 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=200, total=  33.8s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=300 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=300, total=  45.9s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=300 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=300, total=  49.5s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=300 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=300, total=  50.6s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=400 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=400, total= 1.2min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=400 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=400, total= 1.0min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=400 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=400, total= 1.3min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=500 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=500, total= 1.8min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=500 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=500, total= 2.0min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=500 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, n_estimators=500, total= 2.0min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=10 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=10, total=   5.3s\n",
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      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=10, total=   5.2s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=100 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=100, total=  25.2s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=100 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=100, total=  26.8s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=100 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=100, total=  25.3s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=200 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=200, total=  44.7s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=200 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=200, total=  42.5s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=200 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=200, total=  42.6s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=300 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=300, total= 1.1min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=300 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=300, total= 1.1min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=300 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=300, total=  52.7s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=400 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=400, total= 1.2min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=400 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=400, total= 1.2min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=400 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=400, total= 1.5min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=500 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=500, total= 1.8min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=500 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=500, total= 1.9min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=500 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, n_estimators=500, total= 2.3min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=20, n_estimators=10 \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=20, n_estimators=10, total=   6.0s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=20, n_estimators=10 "
     ]
    }
   ],
   "source": [
    "rforest1 = RandomForestClassifier()\n",
    "rfparams = {'n_estimators':[10,100,200,300,400,500], 'criterion':['gini','entropy'], 'max_depth':[10,20,30], 'min_samples_split':[5,10,20], 'min_samples_leaf':[2,4,6]}\n",
    "grid_rf = GridSearchCV(rforest1, rfparams, verbose=2)\n",
    "grid_rf.fit(x_train, y_train)\n",
    "grid_rf.predict(x_test)\n",
    "print(grid_rf.best_score_)\n",
    "print(grid_rf.best_params_)\n",
    "allDone()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "knnparams = {'algorithm':['auto', 'ball_tree', 'kd_tree', 'brute'], 'n_neighbors':[3, 4, 5, 6, 7], 'leaf_size':[10, 20, 30, 40], 'p':[1, 2, 3]}\n",
    "grid_knn = GridSearchCV(neigh1, knnparams, verbose=2)\n",
    "grid_knn.fit(x_train, y_train)\n",
    "grid_knn.predict(x_test)\n",
    "print(grid_knn.best_score_)\n",
    "print(grid_knn.best_params_)\n",
    "allDone()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "svcparams = {'C':[1.0,10,100,1000], 'kernel':['rbf','linear', 'poly', 'sigmoid'], 'degree':[3,4,5], 'gamma':['auto',0.1,0.01,0.001]}\n",
    "grid_svc = GridSearchCV(clf1, svcparams, verbose=2)\n",
    "grid_svc.fit(x_train, y_train)\n",
    "grid_svc.predict(x_test)\n",
    "print(grid_svc.best_score_)\n",
    "print(grid_svc.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JSz_SiuQfj6S"
   },
   "outputs": [],
   "source": [
    "def init():   #initialization function\n",
    "    df2=pd.DataFrame(np.ones((m,d),dtype='int'),columns=features)\n",
    "    return df2\n",
    "#df2=init()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FITNESS FUNCTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ylLmuEVjfj6X"
   },
   "outputs": [],
   "source": [
    "# this defines the fitness of each crow by calculating their accuracy and no features selected using the below formula\n",
    "def fitness(df2):      # fitness function whose 1 parameter takes the crows and their position as input\n",
    "    fnt=np.zeros((m,1))  # fnt is the fitness function\n",
    "    for k in tqdm(range(m)):    \n",
    "        global sel\n",
    "        sel=[]\n",
    "        q=0\n",
    "        imp=0\n",
    "        a=(df2.iloc[k,:])==0.0\n",
    "        if a.all()==True:\n",
    "            df2.iloc[k,:]=1.0\n",
    "        for p in list(features):\n",
    "            if df2.loc[k,p]==1.0:\n",
    "                sel.insert(q,p)\n",
    "                temp=imo[p]\n",
    "                imp=imp+temp\n",
    "            q=q+1\n",
    "        lf=len(sel)\n",
    "        divide=lf/lt\n",
    "        fnt[k]=imp+wf*(1-float(lf/lt))     # fitness formula\n",
    "    return fnt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3JX1sTSufj6c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [02:17<00:00,  6.88s/it]\n"
     ]
    }
   ],
   "source": [
    "z=init()   #crow memory with initial position\n",
    "tmax=50   # Maximum number of iterations (itermax)\n",
    "xn=z.copy()  # the position of the crow\n",
    "mem=init()  # memory is initialized using the init function\n",
    "xnew=(xn).copy()   # the positions of the crow\n",
    "ft=fitness(xn)\n",
    "plotit=[]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FEATURES IMPORTANCE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cRGlsQ57fj6j",
    "outputId": "ecb5e6e9-c09c-4581-d79f-55308fae3400"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [00:01<00:00, 19.11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 182.84it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running fitness\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [01:19<00:00,  3.96s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fitness done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [29:50<00:00, 89.50s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "complete0\n",
      "1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 47.42it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 182.84it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running fitness\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [02:16<00:00,  6.85s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fitness done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<?, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "complete1\n",
      "2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 37.66it/s]\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running fitness\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [02:24<00:00,  7.23s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fitness done\n"
     ]
    },
    {
     "name": "stderr",
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    {
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     "text": [
      "fitness done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<?, ?it/s]\n"
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      "complete39\n",
      "40\n"
     ]
    },
    {
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      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "running fitness\n"
     ]
    },
    {
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      "fitness done\n"
     ]
    },
    {
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      "complete40\n",
      "41\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "running fitness\n"
     ]
    },
    {
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      "fitness done\n"
     ]
    },
    {
     "name": "stderr",
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      "complete41\n",
      "42\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "running fitness\n"
     ]
    },
    {
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      "fitness done\n"
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      "complete42\n",
      "43\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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      "running fitness\n"
     ]
    },
    {
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      "fitness done\n"
     ]
    },
    {
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      "44\n"
     ]
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    {
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     ]
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    {
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     "output_type": "stream",
     "text": [
      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
     ]
    },
    {
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     "output_type": "stream",
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      "running fitness\n"
     ]
    },
    {
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     ]
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      "fitness done\n"
     ]
    },
    {
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      "complete44\n",
      "45\n"
     ]
    },
    {
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    {
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      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
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    {
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      "running fitness\n"
     ]
    },
    {
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      "fitness done\n"
     ]
    },
    {
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     "output_type": "stream",
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      "complete45\n",
      "46\n"
     ]
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    {
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    {
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      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
     ]
    },
    {
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     "output_type": "stream",
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      "running fitness\n"
     ]
    },
    {
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      "fitness done\n"
     ]
    },
    {
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      "complete46\n",
      "47\n"
     ]
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    {
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      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
     ]
    },
    {
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     "output_type": "stream",
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      "running fitness\n"
     ]
    },
    {
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      "fitness done\n"
     ]
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    {
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     "output_type": "stream",
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      "complete47\n",
      "48\n"
     ]
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    {
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    {
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     "output_type": "stream",
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      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "running fitness\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "fitness done\n"
     ]
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    {
     "name": "stderr",
     "output_type": "stream",
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      "complete48\n",
      "49\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
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     "output_type": "stream",
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      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running fitness\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fitness done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "complete49\n",
      "240.6935544695173\n"
     ]
    }
   ],
   "source": [
    "tottime=0\n",
    "# it is a formula to convert positions of each crow into 0 and 1 \n",
    "for t in range(tmax):\n",
    "    print(t)# no of iterations\n",
    "    time1=time.time()\n",
    "   \n",
    "    xnew_list = []\n",
    "    plotit.append(ft.max())\n",
    "    num = random.randint(0,m-1)  \n",
    "   # Generation of random candidate crows for following (chasing)\n",
    "    for i in tqdm(range(m)):   \n",
    "       \n",
    "        r=random.random()\n",
    "        if r>=Ap:\n",
    "            \n",
    "            xnew_list.append((xn.loc[i,:]+fl*r*(mem.loc[num,:]-xn.loc[i,:])).tolist())# Generation of a new position for crow i (state 1)\n",
    "            \n",
    "        else:\n",
    "            \n",
    "            xnew_list.append((xnew.loc[i,:].apply(lambda lam:random.random())).tolist())   # Generation of a new position for crow i (state 2)\n",
    "            \n",
    "            \n",
    "    xnew = pd.DataFrame(xnew_list)\n",
    "    del xnew_list\n",
    "    print('transpose')\n",
    "    xnew= 1/( 1 + np.expm1(( 10*(xnew-0.5))))    #  formula to convert positions of each crow into 0 and 1\n",
    "    print('exp')\n",
    "    xnew=xnew.apply(lambda l:l>=random.random()).astype(int)\n",
    "    print('apply lambda')\n",
    "    xn=xnew.copy()     \n",
    "    print('copied')\n",
    "    # it is the updated binary position of each crow\n",
    "    #feasibility test\n",
    "    # this is done so that to check if atleast one position of the crow has value equal to 1. if it is not true then every\n",
    "    # position value of the crow is made 1.\n",
    "    for i in tqdm(range(m)):\n",
    "        \n",
    "        if ((xn.loc[i,:])==0.0).all()==True:\n",
    "            xn.loc[i,:]=1.0\n",
    "    \n",
    "    print('running fitness')\n",
    "    ft_new=fitness(xn).reshape(ft.shape)# here the accuracy of each crows are calculated.\n",
    "\n",
    "    print('fitness done')\n",
    "    for i in tqdm(range(m)):\n",
    "         \n",
    "        if ft_new[i]>ft[i]:\n",
    "            ft[i]=ft_new[i]\n",
    "            for j in features:\n",
    "                mem.loc[i,j]= xn.loc[i,j]\n",
    "    \n",
    "    print('complete'+str(t))\n",
    "    time2=time.time()\n",
    "    tottime=tottime+(time2-time1)\n",
    "print(tottime/t+1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## WITHOUT GRID SEARCH , WITH BIO-INSPIRED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UWaVv43_fj6r",
    "outputId": "4f74a579-a407-46f5-da84-45edd05a6d62"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|                                                                                           | 0/20 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "0.6591337099811676 0.7080979284369114 0.7419962335216572 0.6836158192090396\n"
     ]
    }
   ],
   "source": [
    "# here the accuracy of each crows are calculated.\n",
    "neigh2 = KNeighborsClassifier()\n",
    "rforest2 =RandomForestClassifier()\n",
    "dtree2 = tree.DecisionTreeClassifier()\n",
    "clf2 = svm.SVC()\n",
    "\n",
    "nacc2=np.zeros((m,1),dtype='float64')\n",
    "rfacc2=np.zeros((m,1),dtype='float64')\n",
    "dtacc2=np.zeros((m,1),dtype='float64')\n",
    "clfacc2=np.zeros((m,1),dtype='float64')\n",
    "\n",
    "q=0\n",
    "for i in tqdm(range(m)):\n",
    "    print(i)\n",
    "    select=[]\n",
    "    for p in list(features):\n",
    "        if mem.loc[i,p]==1:\n",
    "            select.insert(q,p)\n",
    "            q=q+1\n",
    "    neigh2.fit(x_train[select], y_train)\n",
    "    neigh2.predict(x_test[select])\n",
    "    nacc2[i]=neigh2.score(x_test[select],y_test) \n",
    "    print(nacc2[i])\n",
    "    \n",
    "    rforest2.fit(x_train[select], y_train)\n",
    "    rforest2.predict(x_test[select])\n",
    "    rfacc2[i]=rforest2.score(x_test[select],y_test) \n",
    "    print(rfacc2[i])\n",
    "    \n",
    "    dtree2.fit(x_train[select], y_train)\n",
    "    dtree2.predict(x_test[select])\n",
    "    dtacc2[i]=dtree2.score(x_test[select],y_test)\n",
    "    print(dtacc2[i])\n",
    "    \n",
    "    clf2.fit(x_train[select], y_train)\n",
    "    clf2.predict(x_test[select])\n",
    "    clfacc2[i]=clf2.score(x_test[select],y_test)\n",
    "    print(clfacc2[i])\n",
    "\n",
    "print (nacc2.max(), rfacc2.max(), dtacc2.max(),clfacc2.max()) # the crow with maximum accuracy is printed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**SELECTED FEATURED**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pAMS1eJkfj6_",
    "outputId": "f05d8b75-894c-480c-9657-c461161a125b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4146\n",
      "4146\n",
      "4146\n",
      "4146\n"
     ]
    }
   ],
   "source": [
    "print ((mem.loc[nacc1.argmax(),:]==1.0).sum())\n",
    "\n",
    "print ((mem.loc[rfacc1.argmax(),:]==1.0).sum())\n",
    "print ((mem.loc[dtacc1.argmax(),:]==1.0).sum())\n",
    "print ((mem.loc[clfacc1.argmax(),:]==1.0).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "60Wb6XQdfj7f",
    "outputId": "b4b9005b-50bb-4719-d94f-deb161810469"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.9999999999999987, 1.1970472106097965, 1.1970472106097965, 1.1970472106097965, 1.1998847333480596, 1.1998847333480596, 1.1998847333480596, 1.1998847333480596, 1.1998847333480596, 1.1998847333480596, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499, 1.2069418649105499]\n"
     ]
    }
   ],
   "source": [
    "plotse=pd.Series(plotit)\n",
    "print(plotit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wp1Gtqy1fj7p",
    "outputId": "d23b913e-5000-4872-dbf8-807683a8e2d4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2651b463a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(plotse)\n",
    "plt.xlabel('No of iterations')\n",
    "plt.ylabel('Fitness value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "mem.to_csv('memory_MCI-CN.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "myccsa.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
